Customer Analytics

Social network for business purposes


The Global Leaders Index is a research project led by prestigious Swiss business school IMD whose objective is to build a leadership score based on LinkedIn data. The GLI aims at determining the degree to which an individual’s skills, experience and social networks might be a forward indicator of its future leadership. IMD came to us to find an easy way for students to calculate their GLI by leveraging their presence on LinkedIn.

Icon of LinkedIn

Our approach

The first step in designing the methodology was to specify the problem: what are differentiating and discriminating factors that make people leaders? How can we identify these factors through an analysis of LinkedIn data?

A dedicated app embedding a LinkedIn Connect was then built, allowing user data collection and storage. Using a large spectrum of the available data (skills, ego graph, past experiences, educational background, etc.) our algorithm computed a series of 4 indicators, that were then used as building blocks for the Global Leaders Index.:

Graph showing the 4 indicators

Each of these indicators evaluates a specific facet of leadership evaluation. We leveraged various social networks data analysis metrics to calculate each one of those. We particularly took advantage of:

  • The number and nature of relationships (internal friends / external friends, i.e. inside or outside the current company) happened to be a real discriminating feature;
  • Proxys of "betweenness centrality", a metric allowing us to evaluate if users where central for communication in the graph of relations;
  • Variability inside a same function, a same company, or inside a particular sector. This follows the idea that, for instance, accountants with lots of connections might have better capacity to create relationships than salesmen with the same amount of connections.

The output of this work on granular LinkedIn data was the GLI, that helped hundreds of IMD alumni calculate their leadership capabilities, and identify which skills to improve to accelerate their careers.

data analysis metrics network